000941218 001__ 941218
000941218 005__ 20240625095032.0
000941218 0247_ $$2Handle$$a2128/33794
000941218 0247_ $$2URN$$aurn:nbn:de:0001-2023013111
000941218 020__ $$a978-3-95806-641-0
000941218 037__ $$aFZJ-2023-00822
000941218 1001_ $$0P:(DE-Juel1)188287$$aPazem, Josephine$$b0$$eCorresponding author
000941218 245__ $$aDenoising with Quantum Machine Learning$$f- 2022-11-24
000941218 260__ $$aJülich$$bForschungszentrum Jülich GmbH Zentralbibliothek, Verlag$$c2022
000941218 300__ $$a106
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000941218 502__ $$aMasterarbeit, RWTH Aachen University, 2022$$bMasterarbeit$$cRWTH Aachen University$$d2022
000941218 520__ $$aThis master thesis explores aspects of quantum machine learning in the light of an application to dampen the effects of noise on NISQ processors. We investigate the possibility of designing machine learning models that can be accommodated entirely on quantum devices without the help of classical computers. With Dissipative Quantum Neural Networks, we simulate a quantum feed-forward neural network for denoising: the Quantum Autoencoder. We assign it to correct bit-flip noise in states that can exist only quantum mechanically, namely the highly entangled GHZ-states. The numerical simulations report that the QAE can recover the target states up to some tolerance threshold on the noise intensity. To understand the limitations, we investigate the mechanisms behind the completion of the denoising task with quantum entropy measures. The observationsreveal that the latent representation is key to reconstructing the desired state in the outputs. Consequently, we propose an inexpensive modification of the original QAE: the brain box-enhanced QAE. The addition of complexity in the intermediate layers of the network maximizes the robustness of the QAE in a setting where only a finite-size training data set is available. We close the argument with a discussion on the generalization properties of the network.
000941218 536__ $$0G:(DE-HGF)POF4-5224$$a5224 - Quantum Networking (POF4-522)$$cPOF4-522$$fPOF IV$$x0
000941218 8564_ $$uhttps://juser.fz-juelich.de/record/941218/files/Information_82.pdf$$yOpenAccess
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000941218 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188287$$aForschungszentrum Jülich$$b0$$kFZJ
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000941218 9141_ $$y2022
000941218 920__ $$lyes
000941218 9201_ $$0I:(DE-Juel1)PGI-2-20110106$$kPGI-2$$lTheoretische Nanoelektronik$$x0
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